The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure

One of the primary goals of population genetics is to succinctly describe genetic relationships among populations, and the computer program STRUCTURE is one of the most frequently used tools for doing so. The mathematical model used by STRUCTURE was designed to sort individuals into Hardy–Weinberg populations, but the program is also frequently used to group individuals from a large number of populations into a small number of clusters that are supposed to represent the main genetic divisions within species. In this study, I used computer simulations to examine how well STRUCTURE accomplishes this latter task. Simulations of populations that had a simple hierarchical history of fragmentation showed that when there were relatively long divergence times within evolutionary lineages, the clusters created by STRUCTURE were frequently not consistent with the evolutionary history of the populations. These difficulties can be attributed to forcing STRUCTURE to place individuals into too few clusters. Simulations also showed that the clusters produced by STRUCTURE can be strongly influenced by variation in sample size. In some circumstances, STRUCTURE simply put all of the individuals from the largest sample in the same cluster. A reanalysis of human population structure suggests that the problems I identified with STRUCTURE in simulations may have obscured relationships among human populations—particularly genetic similarity between Europeans and some African populations.     

Assessing population genetic structure via the maximisation of genetic distance

Background

The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.

Methods

In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a simulated annealing algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.

Results

The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for F_ST ≥ 0.03, but only STRUCTURE estimates the correct number of clusters for F_ST as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.

Conclusion

This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.     

Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci

Accurate detection of offspring resulting from hybridization between individuals of distinct populations has a range of applications in conservation and population genetics. We assessed the hybrid identification efficiency of two methods (implemented in the STRUCTURE and NEWHYBRIDS programs) which are tailored to identifying hybrid individuals but use different approaches. Simulated first- and second-generation hybrids were used to assess the performance of these two methods in detecting recent hybridization under scenarios with different levels of genetic divergence and varying numbers of loci. Despite the different approaches of the methods, the hybrid detection efficiency was generally similar and neither of the two methods outperformed the other in all scenarios assessed. Interestingly, hybrid detection efficiency was only minimally affected by whether reference population allele frequency information was included or not. In terms of genotyping effort, efficient detection of F1 hybrid individuals requires the use of 12 or 24 loci with pairwise F(ST) between hybridizing parental populations of 0.21 or 0.12, respectively. While achievable, these locus numbers are nevertheless higher than the number of loci currently commonly applied in population genetic studies. The method of STRUCTURE seemed to be less sensitive to the proportion of hybrids included in the sample, while NEWHYBRIDS seemed to perform slightly better when individuals from both backcross and F1 hybrid classes were present in the sample. However, separating backcrosses from purebred parental individuals requires a considerable genotyping effort (at least 48 loci), even when divergence between parental populations is high.     

The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem

Inferences of population structure and more precisely the identification of genetically homogeneous groups of individuals are essential to the fields of ecology, evolutionary biology and conservation biology. Such population structure inferences are routinely investigated via the program structure implementing a Bayesian algorithm to identify groups of individuals at Hardy–Weinberg and linkage equilibrium. While the method is performing relatively well under various population models with even sampling between subpopulations, the robustness of the method to uneven sample size between subpopulations and/or hierarchical levels of population structure has not yet been tested despite being commonly encountered in empirical data sets. In this study, I used simulated and empirical microsatellite data sets to investigate the impact of uneven sample size between subpopulations and/or hierarchical levels of population structure on the detected population structure. The results demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure and downward‐biased estimates of the true number of subpopulations. Distinct subpopulations with reduced sampling tended to be merged together, while at the same time, individuals from extensively sampled subpopulations were generally split, despite belonging to the same panmictic population. Four new supervised methods to detect the number of clusters were developed and tested as part of this study and were found to outperform the existing methods using both evenly and unevenly sampled data sets. Additionally, a subsampling strategy aiming to reduce sampling unevenness between subpopulations is presented and tested. These results altogether demonstrate that when sampling evenness is accounted for, the detection of the correct population structure is greatly improved.     

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious “populations”. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation

Traditional methods for characterizing genetic differentiation among populations rely on a priori grouping of individuals. Bayesian clustering methods avoid this limitation by using linkage and Hardy–Weinberg disequilibrium to decompose a sample of individuals into genetically distinct groups. There are several software programs available for Bayesian clustering analyses, all of which describe a decrease in the ability to detect distinct clusters as levels of genetic differentiation among populations decrease. However, no study has yet compared the performance of such methods at low levels of population differentiation, which may be common in species where populations have experienced recent separation or high levels of gene flow. We used simulated data to evaluate the performance of three Bayesian clustering software programs, PARTITION, STRUCTURE, and BAPS, at levels of population differentiation below F _ST=0.1. PARTITION was unable to correctly identify the number of subpopulations until levels of F _ST reached around 0.09. Both STRUCTURE and BAPS performed very well at low levels of population differentiation, and were able to correctly identify the number of subpopulations at F _ST around 0.03. The average proportion of an individual’s genome assigned to its true population of origin increased with increasing F _ST for both programs, reaching over 92% at an F _ST of 0.05. The average number of misassignments (assignments to the incorrect subpopulation) continued to decrease as F _ST increased, and when F _ST was 0.05, fewer than 3% of individuals were misassigned using either program. Both STRUCTURE and BAPS worked extremely well for inferring the number of clusters when clusters were not well-differentiated (F _ST=0.02–0.03), but our results suggest that F _ST must be at least 0.05 to reach an assignment accuracy of greater than 97%.     

Emergence of spatially structured populations by area‐concentrated search

The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual‐based simulations in continuous space, we explored the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area‐concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated the effects of different resource clustering pattern in landscapes (single large cluster vs. many small clusters) and different resource density on the spatial structure of populations and movement between resource clusters of individuals. As results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured populations with individuals concentrated in areas of high resource density, searching within areas of resources, and “dispersing” in straight line between resource patches. “Emigration” was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. “Looping” from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of such spatial structure.     

A parsimony estimator of the number of populations from a STRUCTURE‐like analysis

Population genetics model based Bayesian methods have been proposed and widely applied to making unsupervised inference of population structure from a sample of multilocus genotypes. Usually they provide good estimates of the ancestry (or population membership) of sampled individuals by clustering them probabilistically or proportionally into (anonymous) populations. However, they have difficulties in accurately estimating the number of populations (K) represented by the sampled individuals. This study proposed a new ad hoc estimator of K, calculable from the output of a population clustering program such as STRUCTURE or ADMIXTURE. The new criterion, called parsimony index (PI), aims to identify the number of populations (K) which yields consistently the minimal admixture estimates of sampled individuals. Extensive simulated and empirical data were used to compare the accuracy of PI and two popular K estimators based on Pr[X|K] (i.e., the probability of genotype data X given K) and ΔK (i.e., the rate of change of the probability of data as a function of K) calculated from STRUCTURE outputs, and the accuracy of PI and the cross‐validation method calculated from ADMIXTURE outputs. It was shown that PI was more accurate than the other methods consistently in various population structure (e.g., hierarchical island model, different extents of differentiation) and sampling (e.g., unbalanced sample sizes, different marker information contents) scenarios. The ΔK method was more accurate than the Pr[X|K] method only for hierarchically structured or highly inbred populations, and the opposite was true in the other scenarios. The PI method was implemented in a computer program, KFinder, which can be run on all major computer platforms.     

Population genetic structure and long-distance dispersal among seabird populations: implications for colony persistence

Dramatic local population decline brought about by anthropogenic-driven change is an increasingly common threat to biodiversity. Seabird life history traits make them particularly vulnerable to such change; therefore, understanding population connectivity and dispersal dynamics is vital for successful management. Our study used a 357-base pair mitochondrial control region locus sequenced for 103 individuals and 18 nuclear microsatellite loci genotyped for 245 individuals to investigate population structure in the Atlantic and Pacific populations of the pelagic seabird, Leach's storm-petrel Oceanodroma leucorhoa leucorhoa. This species is under intense predation pressure at one regionally important colony on St Kilda, Scotland, where a disparity between population decline and predation rates hints at immigration from other large colonies. AMOVA, F(ST), Φ(ST) and Bayesian cluster analyses revealed no genetic structure among Atlantic colonies (Global Φ(ST) = -0.02 P > 0.05, Global F(ST) = 0.003, P > 0.05, STRUCTURE K = 1), consistent with either contemporary gene flow or strong historical association within the ocean basin. The Pacific and Atlantic populations are genetically distinct (Global Φ(ST) = 0.32 P < 0.0001, Global F(ST) = 0.04, P < 0.0001, STRUCTURE K = 2), but evidence for interocean exchange was found with individual exclusion/assignment and population coalescent analyses. These findings highlight the importance of conserving multiple colonies at a number of different sites and suggest that management of this seabird may be best viewed at an oceanic scale. Moreover, our study provides an illustration of how long-distance movement may ameliorate the potentially deleterious impacts of localized environmental change, although direct measures of dispersal are still required to better understand this process.     

青海省东部地区喜马拉雅旱獭种群遗传结构

本研究利用11个微卫星标记对采自青海省东部地区的13个喜马拉雅旱獭（Marmota himalayana）种群149个个体进行了基因分型,并运用种群遗传学方法对其遗传多样性和遗传结构进行分析。结果显示,11个微卫星标记位点共计检测到97个等位基因,各种群的平均观测杂合度和期望杂合度范围分别为0.58~0.82和0.60~0.79,种群遗传多样性水平相对较高;遗传结构分析表明,青海省东部地区的喜马拉雅旱獭种群具有显著的遗传结构,13个地理种群形成了3个遗传聚类群,且3个遗传聚类群与湟水河和黄河上游干流所划分出的地理单元完全一致,因此我们认为湟水河和黄河上游干流是阻碍该地区喜马拉雅旱獭种群进行迁移扩散和基因交流的天然屏障。同时,STRUCTURE分析结果还显示3个遗传聚类群间仍有明显的基因流,AMOVA分析也显示3个聚类群间变异百分比为6.60%,仅略高于聚类群内种群间的变异（4.51%）,而远低于种群内变异水平（88.90%）,表明三个聚类群间的分化程度并不是很深。这说明喜马拉雅旱獭可能通过桥梁或在枯水期等穿越河流进行基因交流。以上结果为该地区的旱獭种群监控和鼠疫防控提供了科学的理论基础。     

How many marker loci are necessary? Analysis of dominant marker data sets using two popular population genetic algorithms

The number of marker loci required to answer a given research question satisfactorily is especially important for dominant markers since they have a lower information content than co‐dominant marker systems. In this study, we used simulated dominant marker data sets to determine the number of dominant marker loci needed to obtain satisfactory results from two popular population genetic analyses: STRUCTURE and AMOVA (analysis of molecular variance). Factors such as migration, level of population differentiation, and unequal sampling were varied in the data sets to mirror a range of realistic research scenarios. AMOVA performed well under all scenarios with a modest quantity of markers while STRUCTURE required a greater number, especially when populations were closely related. The popular ΔK method of determining the number of genetically distinct groups worked well when sampling was balanced, but underestimated the true number of groups with unbalanced sampling. These results provide a window through which to interpret previous work with dominant markers and we provide a protocol for determining the number of markers needed for future dominant marker studies.     

A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multilocus genotype data

Nonrandom mating induces correlations in allelic states within and among loci that can be exploited to understand the genetic structure of natural populations (Wright 1965). For many species, it is of considerable interest to quantify the contribution of two forms of nonrandom mating to patterns of standing genetic variation: inbreeding (mating among relatives) and population substructure (limited dispersal of gametes). Here, we extend the popular Bayesian clustering approach STRUCTURE (Pritchard et al. 2000) for simultaneous inference of inbreeding or selfing rates and population-of-origin classification using multilocus genetic markers. This is accomplished by eliminating the assumption of Hardy-Weinberg equilibrium within clusters and, instead, calculating expected genotype frequencies on the basis of inbreeding or selfing rates. We demonstrate the need for such an extension by showing that selfing leads to spurious signals of population substructure using the standard STRUCTURE algorithm with a bias toward spurious signals of admixture. We gauge the performance of our method using extensive coalescent simulations and demonstrate that our approach can correct for this bias. We also apply our approach to understanding the population structure of the wild relative of domesticated rice, Oryza rufipogon, an important partially selfing grass species. Using a sample of n = 16 individuals sequenced at 111 random loci, we find strong evidence for existence of two subpopulations, which correlates well with geographic location of sampling, and estimate selfing rates for both groups that are consistent with estimates from experimental data (s approximately 0.48-0.70).     

Genetic spatial autocorrelation can readily detect sex-biased dispersal

Sex-biased dispersal is expected to generate differences in the fine-scale genetic structure of males and females. Therefore, spatial analyses of multilocus genotypes may offer a powerful approach for detecting sex-biased dispersal in natural populations. However, the effects of sex-biased dispersal on fine-scale genetic structure have not been explored. We used simulations and multilocus spatial autocorrelation analysis to investigate how sex-biased dispersal influences fine-scale genetic structure. We evaluated three statistical tests for detecting sex-biased dispersal: bootstrap confidence intervals about autocorrelation r values and recently developed heterogeneity tests at the distance class and whole correlogram levels. Even modest sex bias in dispersal resulted in significantly different fine-scale spatial autocorrelation patterns between the sexes. This was particularly evident when dispersal was strongly restricted in the less-dispersing sex (mean distance <200 m), when differences between the sexes were readily detected over short distances. All tests had high power to detect sex-biased dispersal with large sample sizes (n ≥ 250). However, there was variation in type I error rates among the tests, for which we offer specific recommendations. We found congruence between simulation predictions and empirical data from the agile antechinus, a species that exhibits male-biased dispersal, confirming the power of individual-based genetic analysis to provide insights into asymmetries in male and female dispersal. Our key recommendations for using multilocus spatial autocorrelation analyses to test for sex-biased dispersal are: (i) maximize sample size, not locus number; (ii) concentrate sampling within the scale of positive structure; (iii) evaluate several distance class sizes; (iv) use appropriate methods when combining data from multiple populations; (v) compare the appropriate groups of individuals.     

基于微流控芯片的鲑科鱼类单核苷酸多态性分型系统构建

为开发针对大规模样本、低通量位点的单核苷酸多态性(Single nucleotide polymorphism, SNP)分型技术,研究依据虹鳟高通量SNP芯片检测鲑科4个属不同物种群体样本的结果,筛选获得了96个高质量共享多态性位点,应用Fluidigm 96.96微流控动态芯片平台,构建了用于鲑科物种增殖放流个体识别的SNP分型系统。以细鳞鲑为例评估芯片分型结果可靠性,分型成功率为98.63%,与Affymetrix高通量芯片分型一致性达到97.92%。基于该芯片分型结果,使用CERVUS 3.0.7软件对96尾细鳞鲑子代样本及其候选亲本和干扰亲本进行亲权鉴定,结果能够准确重现复杂家系的真实系谱,在用于单亲本亲权鉴定时,第一亲本非排除率(Nonexclusion probability for first parent, NE-1P)为4.362×10^–4,用于双亲本亲权鉴定时,双亲非排除率(Nonexclusion probability for parent pair, NE-PP)为6.538×10^–12,完全满足增殖放流回捕个体分...     

Inbreeding and kinship in the ant Plagiolepis pygmaea

In ants the presence of multiple reproductive queens (polygyny) decreases the relatedness among workers and the brood they rear, and subsequently dilutes their inclusive fitness benefits from helping. However, adoption of colony daughters, low male dispersal in conjunction with intranidal (within nest) mating and colony reproduction by budding may preserve local genetic differences, and slow down the erosion of relatedness. Reduced dispersal and intranidal mating may, however, also lead to detrimental effects owing to competition and inbreeding. We studied mating and dispersal patterns, and colony kinship in three populations of the polygynous ant Plagiolepis pygmaea using microsatellite markers. We found that the populations were genetically differentiated, but also a considerable degree of genetic structuring within populations. The genetic viscosity within populations can be attributed to few genetically homogeneous colony networks, which presumably have arisen through colony reproduction by budding. Hence, selection may act at different levels, the individuals, the colonies and colony networks. All populations were also significantly inbred (F=0.265) suggesting high frequencies of intranidal mating and low male dispersal. Consequently the mean regression relatedness among workers was significantly higher (r = 0.529-0.546) than would be expected under the typically reported number (5-35) of queens in nests of the species. Furthermore, new queens were mainly recruited from their natal or a neighbouring related colony. Finally, the effective number of queens coincided with that found upon excavation, suggesting low reproductive skew.     

Genetic and ecological data provide incongruent interpretations of population structure and dispersal in naturally subdivided populations of white-tailed ptarmigan (Lagopus leucura)

The dispersal of individuals among populations affects the demographic and adaptive trajectories of animal populations and is fundamental to understanding population dynamics. White-tailed ptarmigan (Lagopus leucura) are a high elevation grouse species that live year-round in patchily distributed alpine areas in western North America. We investigated the patterns of dispersal and identified barriers to gene flow for a threatened subspecies (L. l. saxatilis) endemic to Vancouver Island, Canada. Connectivity among seven sites was examined using nine microsatellite loci (n = 133 individuals, H(O) = 0.62, mean number of alleles = 10) and direct movement observations using radio-telemetry (n = 118 individuals). Average movement distances of individuals measured by radio-telemetry were 0.63-3.23 km and considerably less than the shortest distance between sampling sites (18 km). Furthermore, despite extensive radio-telemetry data, movement was never observed between any of the seven sampling sites. In contrast, genetic results (STRUCTURE, TESS) showed connectivity among most of the seven sampling sites and suggested that genetic variation is best explained by two clusters of individuals which separated the South sampling site from all other areas of Vancouver Island. Analysis of molecular data also showed a generally consistent pattern of isolation by distance (Mantel test r = 0.11, P < 0.01) with large areas of unsuitable low elevation habitat possibly acting as barriers to gene flow. Despite the naturally subdivided distribution of populations, white-tailed ptarmigan do not fit well into any common definition of a metapopulation. We conclude the incongruities between the genetic and radio-telemetry data are best explained by episodic dispersal patterns. In this study, we demonstrated the importance of combining genetic and ecological data in understanding patterns of dispersal and population structure.     

Comparative analysis of AFLPs and SSRs efficiency in resolving population genetic structure of Mediterranean Solea vulgaris

The performance of different molecular markers in the assessment of population structure was tested using samples of Solea vulgaris collected in the Mediterranean within and outside the hypothetical dispersal ability of the species. A total of 172 individuals belonging to four population samples were analysed using 15 microsatellites [simple sequence repeats (SSRs)] and 153 amplified fragment length polymorphisms (AFLPs). Considering the global qualitative patterns, we found a correlation between SSRs and AFLPs in detecting genetic differentiation among samples. However, on a small geographical scale, AFLPs were able to discriminate individuals from neighbouring populations whereas SSRs were not, and the percentage of individuals correctly assigned to their population of origin was higher with AFLPs than with SSRs. The high number of loci analysed with the AFLP technique could increase the probability to include outlier loci in the analysis; however, the neutrality test performed on our data set did not show evidence of selection acting on the S. vulgaris samples. Even if the choice of the molecular marker depends mainly on the biological question to be addressed, the higher power of discrimination and the comparative technical ease of obtaining data from AFLPs with respect to SSRs suggest the use of AFLPs for many population genetics studies.     

Genetic admixture between captive-bred and wild individuals affects patterns of dispersal in a brown trout (<Emphasis Type="Italic">Salmo trutta</Emphasis>) population

Genetic admixture between captive-bred and wild individuals has been demonstrated to affect many individual traits, although little is known about its potential influence on dispersal, an important trait governing the eco-evolutionary dynamics of populations. Here, we quantified and described the spatial distribution of genetic admixture in a brown trout (Salmo trutta) population from a small watershed that was stocked until 1999, and then tested whether or not individual dispersal parameters were related to admixture between wild and captive-bred fish. We genotyped 715 fish at 17 microsatellite loci sampled from both the mainstream and all populated tributaries, as well as 48 fish from the hatchery used to stock the study area. First, we used Bayesian clustering to infer local genetic structure and to quantify genetic admixture. We inferred first generation migrants to identify dispersal events and test which features (genetic admixture, sex and body length) affected dispersal parameters (i.e. probability to disperse, distance of dispersal and direction of the dispersal event). We identified two genetic clusters in the river basin, corresponding to wild fish on the one hand and to fish derived from the captive strain on the other hand, allowing us to define an individual gradient of admixture. Individuals with a strong assignment to the captive strain occurred almost exclusively in some tributaries, and were more likely to disperse towards a tributary than towards a site of the mainstream. Furthermore, dispersal probability increased as the probability of assignment to the captive strain increased, and individuals with an intermediate level of admixture exhibited the lowest dispersal distances. These findings show that various dispersal parameters may be biased by admixture with captive-bred genotypes, and that management policies should take into account the differential spread of captive-bred individuals in wild populations.     

A genetic analysis of dragonfly population structure

Dragonflies reside in both aquatic and terrestrial environments, depending on their life stage, necessitating the conservation of drastically different habitats; however, little is understood about how nymph and adult dragonflies function as metapopulations within connected habitat. We used genetic techniques to examine nymphs and adults within a single metapopulation both spatially and temporally to better understand metapopulation structure and the processes that might influence said structure. We sampled 97 nymphs and 149 adult Sympetrum obtrusum from eight locations, four aquatic, and four terrestrial, at the Pierce Cedar Creek Institute in Southwest Michigan over two summers. We performed AFLP genetic analysis and used the Bayesian analysis program STRUCTURE to detect genetic clusters from sampled individuals. STRUCTURE detected k = u4 populations, in which nymphs and adults from the same locations collected in different years did not necessarily fall into the same clusters. We also evaluated grouping using the statistical clustering analyses NMDS and MRPP. The results of these confirmed findings from STRUCTURE and emphasized differences between adults collected in 2012 and all other generations. These results suggest that both dispersal and a temporal cycle of emergence of nymphs from unique clusters every other year could be influential in structuring dragonfly populations, although our methods were not able to fully distinguish the influences of either force. This study provides a better understanding of local dragonfly metapopulation structure and provides a starting point for future studies to investigate the spatial and temporal mechanisms controlling metapopulation structure. The results of the study should prove informative for managers working to preserve genetic diversity in connected dragonfly metapopulations, especially in the face of increasing anthropogenic landscape changes.     

侯晨曦：附表1-附表2

本研究基于7个叶绿体DNA片段（cpDNA）和2个核DNA片段（ITS和PZ8）的测序数据,对龙血树柴胡（Bupleurum dracaenoides Huan C.Wang,Z.R.He&H.Sun）8个居群的153个样本进行了遗传多样性和分布式样研究。cpDNA片段分析结果显示：龙血树柴胡在物种水平具有较高的遗传多样性（H_d=0.862;P_i=0.00567）,但居群内遗传多样性低,遗传变异主要存在于居群间,遗传分化显著（F_st=0.959）;而核DNA片段ITS和PZ8的数据分析结果显示,其遗传多样性较低（H_d=0.532,P_i =0.00121和H_d=0.349,P_i=0.00060）,遗传变异主要存在于居群内,居群间仅存在一定程度的遗传分化。中性检验和失配分布分析结果发现龙血树柴胡没有经历过近期种群扩张事件,8个居群的153个样本从遗传成份上可被分为两组。研究结果将为龙血树柴胡的资源保护和发掘提供参考。